James P. Johnson - Proteomics LIMMA Regression - v3-7-23

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#Load some useful libraries
library(limma)
library(DESeq2)
library(edgeR)
library(data.table)
library(tidyr)
library(magrittr)
library(tidyverse)
library(sjlabelled)
library(sjmisc)
library(ggplot2)
library(broom)
library(DataCombine)
library(dplyr)
library(ggpubr)
library(stringr)
library(ggprism)
library(patchwork)
library(magrittr)
library(ggbeeswarm)
library(data.table)

#import data
countdf <- DropNA(read.csv(file = 'proteomics.csv'))

No Var specified. Dropping all NAs from the data frame.

0 rows dropped from the data frame because of missing values.

rownames(countdf) <- NULL
meta <- DropNA(read.csv(file = 'proteomics_metadata.csv'))

No Var specified. Dropping all NAs from the data frame.

0 rows dropped from the data frame because of missing values.

rownames(meta) <- NULL
anno <- DropNA(read.csv(file = 'proteomics_metadata_table.csv'))

No Var specified. Dropping all NAs from the data frame.

0 rows dropped from the data frame because of missing values.

rownames(anno) <- NULL
full <- DropNA(read.csv(file = 'proteomics_full.csv'))

No Var specified. Dropping all NAs from the data frame.

0 rows dropped from the data frame because of missing values.

rownames(full) <- NULL

#clean-up tables
#prepare for model
#make consistent index/column/label/ordering between tables/matrices
names(countdf)[1] <- 'name'
names(countdf) <- sapply(str_remove_all(names(countdf),"X"),"[") # remove added X's from PCI names
count_matrix <- countdf[,2:ncol(countdf)]
count_matrix <- as.matrix(count_matrix)
names(full) <- sapply(str_remove_all(colnames(full),"X"),"[")
names(count_matrix) <- sapply(str_remove_all(colnames(count_matrix),"X"),"[")
meta$sex <- factor(meta$sex)
full$sex <- factor(full$sex)
meta$bowel <- factor(as.numeric(factor(meta$bowel, levels = c(1,2,3,4), labels = c(1,2,3,4))))
full$bowel <- factor(as.numeric(factor(full$bowel, levels = c(1,2,3,4), labels = c(1,2,3,4))))
meta <- within(meta, bowel <- relevel(bowel, ref = 3))
full <- within(full, bowel <- relevel(bowel, ref = 3))
count_matrix

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         01788805   01789415  01789593  01789705   01790494   01790845  01790891
         01791313   01793432  01793692   01794557  01794702  01795699   01795962
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         01802964  01803225   01803627   01804135   01804284  01806149  01806152
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        01903562   01903748   01905229   01905932   01906832  01906856   01907066
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         01909807   01910034   01911124   01911172  01911364   01911370  01912726
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        01916479   01916558   01917148   01917153  01917395  01918227  01918540
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         01926705  01927157  01927181   01927185   01927208   01928201   01929506
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         01931898   01932120  01932384  01932611  01932693   01933205   01933291
         01934177   01934222  01935644   01935829  01935930   01936132   01937405
         01937921   01938378  01938606  01939058  01939189  01939555   01940575
         01940618   01941086  01941134   01941857  01942853  01943201   01943712
        01944942   01945564   01946056   01946852   01947387  01947733   01948018
         01948580   01948634   01949838   01950342   01952180  01952330   01953116
        01953398   01954001  01954492  01954615   01954804   01954947  01955210
         01955250  01955427   01955611   01956344   01958214   01958339   01959030
         01959080   01959804  01959844   01959943   01960296  01960682   01960736
         01960876  01962038   01962253   01962385  01962646  01963140   01963243
        01963556   01964190  01964339  01965364   01965921   01965975   01966606
         01966838   01968285  01968623  01969364  01969821  01970438   01971060
        01971659   01971921  01972000   01972700   01974110  01974625   01974833
         01975030   01975130   01975702   01976404   01976567  01980181  01980311
         01980444  01980931  01981511  01982074   01982352   01982907   01983318
         01984058   01984497   01984762   01984864   01984952   01985328   01985884
         01986408  01987388  01987914  01988559  01988631  01989393   01989933
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        01994483   01995018  01995109  01995589   01995656   01997414  01997759
        01997826   01997940   01998370   01998451   01998999  01999351   H344502
         H409129   H460562   H794171
 [ reached getOption("max.print") -- omitted 274 rows ]
attr(,"names")
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   [8] "01003758" "01004037" "01004350" "01004551" "01004575" "01004596" "01005890"
  [15] "01005935" "01006225" "01007074" "01007520" "01008097" "01008265" "01008612"
  [22] "01008631" "01009390" "01009735" "01010189" "01010305" "01010353" "01010869"
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  [36] "01015440" "01016929" "01017180" "01017741" "01017775" "01018937" "01019058"
  [43] "01019071" "01020175" "01020229" "01021265" "01022339" "01022402" "01022407"
  [50] "01022858" "01023349" "01023413" "01024256" "01024456" "01024630" "01025234"
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  [71] "01034215" "01034290" "01034766" "01035670" "01035805" "01036226" "01037387"
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  [85] "01041616" "01041867" "01042050" "01043932" "01044249" "01044589" "01046903"
  [92] "01047354" "01047811" "01047962" "01048336" "01048590" "01049187" "01049423"
  [99] "01049882" "01050730" "01051449" "01051853" "01052106" "01052129" "01052191"
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 [134] "01065558" "01066219" "01066842" "01068049" "01068336" "01068343" "01068688"
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 [190] "01099022" "01099707" "01099807" "01100090" "01100093" "01100301" "01100470"
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 [218] "01114773" "01115185" "01116419" "01116698" "01117506" "01117666" "01118407"
 [225] "01118887" "01119219" "01119278" "01119799" "01121094" "01121281" "01121506"
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 [260] "01134627" "01134852" "01134952" "01135208" "01135229" "01135434" "01135564"
 [267] "01137439" "01137764" "01138494" "01139462" "01139724" "01139766" "01140117"
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 [442] "01216764" "01217376" "01217679" "01218073" "01218722" "01219514" "01220660"
 [449] "01220747" "01221266" "01221452" "01221476" "01221559" "01221615" "01221688"
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 [463] "01224606" "01225255" "01226325" "01226780" "01226903" "01227044" "01227858"
 [470] "01228251" "01228476" "01230087" "01230333" "01230573" "01230961" "01231502"
 [477] "01231615" "01231638" "01231872" "01232606" "01232614" "01233310" "01233390"
 [484] "01233782" "01234668" "01236052" "01236168" "01236520" "01236818" "01238327"
 [491] "01238340" "01238425" "01238758" "01239027" "01239160" "01239686" "01240429"
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 [722] "01355617" "01355791" "01356466" "01357653" "01357953" "01358815" "01358940"
 [729] "01359817" "01359989" "01360278" "01360318" "01360677" "01361259" "01361414"
 [736] "01361790" "01362364" "01362687" "01363795" "01364126" "01364192" "01364721"
 [743] "01365052" "01365832" "01366208" "01366706" "01366783" "01367738" "01367896"
 [750] "01367974" "01368088" "01368213" "01368780" "01369634" "01370236" "01370608"
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 [820] "01404344" "01404357" "01405352" "01405518" "01405558" "01405967" "01407492"
 [827] "01409276" "01409471" "01410286" "01411311" "01411551" "01411874" "01411965"
 [834] "01412088" "01412110" "01412199" "01414715" "01414861" "01415267" "01416835"
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 [848] "01425137" "01425460" "01426287" "01426848" "01427224" "01427331" "01428031"
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 [967] "01489961" "01491050" "01491245" "01491438" "01492075" "01492335" "01492554"
 [974] "01493928" "01494796" "01495022" "01496084" "01496511" "01497137" "01497187"
 [981] "01497207" "01497564" "01498008" "01498133" "01498419" "01498803" "01498819"
 [988] "01499133" "01500041" "01500706" "01500905" "01501052" "01501394" "01502061"
 [995] "01502166" "01502372" "01502665" "01502955" "01503152" "01503991"
 [ reached getOption("max.print") -- omitted 546726 entries ]

countdf

meta

anno

full

# Design linear regression models using lmFit and eBayes with LIMMA
# This code adapted from Christian Diener, PhD:
design <- model.matrix(~bowel + sex + age + BMI_CALC + eGFR, meta) # Covariates: sex, age, BMI
#dge <- DGEList(counts=count_matrix)  # Where `count_matrix` is the matrix mentioned above
#dge <- calcNormFactors(count_matrix)  # Normalize the matrix (this step only for CORNCOB/microbiome data)
#logCPM <- cpm(dge, log=FALSE)  # Takes the log of the data
fit <- lmFit(count_matrix, design)  # Fits the model for all metabolites
fit <- eBayes(fit)  # Stabilizes the variances

#Pre-process for plotting:
#Get results table for Constipation coefficient relative to High Normal BMF:
re_const <- topTable(fit, coef = 2, genelist = anno, sort="p", number="none")  # Select the significant models by coefficient 2

indices <- match(rownames(re_const), rownames(anno)) # associate column of protein names with index of protein
re_const[1] <- anno$name[indices] # associate the protein names with the protein indices
re_const <- dplyr::inner_join(anno, re_const, by = intersect(names(anno),names(re_const))) #combine anno and re dfs by intersection of protein values
p_const <- re_const[re_const$adj.P.Val < 0.05,] # create df of just significant adj P value results
p_const <- p_const[order(p_const$adj.P.Val),] # order by adj P value
p_const <- p_const[,c('logFC','B','adj.P.Val','name','panel','uniprot','gene_name','gene_description','P.Value')] # keep only desired columns

#Pre-process for plotting:
#Get results table for Low Normal coefficient relative to High Normal BMF:
re_low <- topTable(fit, coef = 3, genelist = anno, sort="p", number="none")  # Select the significant models by coefficient 3

indices <- match(rownames(re_low), rownames(anno)) # associate column of protein names with index of protein
re_low[1] <- anno$name[indices] # associate the protein names with the protein indices
re_low <- dplyr::inner_join(anno, re_low, by = intersect(names(anno),names(re_low))) #combine anno and re dfs by intersection of protein values
p_low <- re_low[re_low$adj.P.Val < 0.05,] # create df of just significant adj P value results
p_low <- p_low[order(p_low$adj.P.Val),] # order by adj P value
p_low <- p_low[,c('logFC','B','adj.P.Val','name','panel','uniprot','gene_name','gene_description','P.Value')] # keep only desired columns

#Pre-process for plotting:
#Get results table for Diarrhea coefficient relative to High Normal BMF:
re_diarrhea <- topTable(fit, coef = 4, genelist = anno, sort="p", number="none")  # Select the significant models by coefficient 4

indices <- match(rownames(re_diarrhea), rownames(anno)) # associate column of protein names with index of protein
re_diarrhea[1] <- anno$name[indices] # associate the protein names with the protein indices
re_diarrhea <- dplyr::inner_join(anno, re_diarrhea, by = intersect(names(anno),names(re_diarrhea))) #combine anno and re dfs by intersection of protein values
p_diarrhea <- re_diarrhea[re_diarrhea$adj.P.Val < 0.05,] # create df of just significant adj P value results
p_diarrhea <- p_diarrhea[order(p_diarrhea$adj.P.Val),] # order by adj P value
p_diarrhea <- p_diarrhea[,c('logFC','B','adj.P.Val','name','panel','uniprot','gene_name','gene_description','P.Value')] # keep only desired columns



#Show dfs of significant hits (there are none)
sig_const <- p_const[which(p_const$adj.P.Val < 0.05),]
sig_low <- p_low[which(p_low$adj.P.Val < 0.05),]
sig_diarrhea <- p_diarrhea[which(p_diarrhea$adj.P.Val < 0.05),]
rbind(sig_const,sig_low,sig_diarrhea)

#Prepare for plotting:
comparisons = list(c("Low Normal","High Normal"),c("Diarrhea","High Normal"))
sig_low['bowel'] <- rep('Low Normal',1)
sig_diarrhea['bowel']  <- rep('Diarrhea',25)
bound <- rbind(sig_low,sig_diarrhea)
a <- full[,names(full) %in% bound['name'][[1]]]
setcolorder(a,bound['name'][[1]])
names(a) <- bound['gene_name'][[1]]
biochemistry <- cbind(full[,1:6],a)
names(biochemistry)[11] <- 'TNFRSF11B (2nd Instance)'
biochemistry$bowel <- factor(biochemistry$bowel, levels=c(1,2,3,4), labels = c("Constipation","Low Normal","High Normal","Diarrhea"))
biochemistry$bowel <- factor(biochemistry$bowel, levels=c("Constipation","Low Normal","High Normal","Diarrhea"), labels = c("Constipation","Low Normal","High Normal","Diarrhea"))


#Annotation function:
sig = function(x){
  if(x < 0.001){"***"} 
  else if(x < 0.01){"**"}
  else if(x < 0.05){"*"}
  else{NA}}

#Plotting function:
test = function(x,y,z,j) {
  x = NULL
  y = NULL
  if (counter == 1) {
    z = comparisons[[1]][1]
  } else {
    z = comparisons[[2]][1]
    counter<<-0
  }
  print(j)
  if (str_detect(z,"Diarrhea") & any(sig_diarrhea[,'gene_name']==j)) {
    results = list(p.value = sig_diarrhea[which(sig_diarrhea[,'gene_name']==j),]['adj.P.Val'][[1]])
  } else if (str_detect(z,"Low Normal") & any(sig_low[,'gene_name']==j)) {
    results = list(p.value = sig_low[which(sig_low[,'gene_name']==j),]['adj.P.Val'][[1]])
  } else {
    results = list(p.value = 1)
  }
  names(results) <- 'p.value'
  counter <<-counter+1
  return(results)
}

#Begin accruing plots:
counter <<-1
myplots <- list()  # new empty list
  for (ind in 1:(ncol(biochemistry)-6)) {
  myplots[[ind]] <- local({
    label = paste(names(biochemistry)[ind+6],sep="")
    sub = ifelse(label!='TNFRSF11B (2nd Instance)',paste(bound['gene_description'][which(bound['gene_name']==label),]),paste(bound['gene_description'][which(bound['gene_name']=='TNFRSF11B'),]))
    print(label)
    print(sub)
    n = 2
    plotlim_lower = min(biochemistry[!is.na(biochemistry[,ind+6]),][,ind+6])
    plotlim_upper = max(biochemistry[!is.na(biochemistry[,ind+6]),][,ind+6])
    plotlim_bar = plotlim_lower - n
    plotlim_margin = abs(plotlim_bar - n*10)
    sublabel <- 'TNFRSF11B'
    plt <- ggplot(data = biochemistry, aes(x = bowel, y = .data[[label]], group = bowel)) +
    scale_x_discrete(guide = guide_axis(n.dodge = 2))+
    geom_beeswarm(aes(color = bowel), size = 0.1, cex = 0.5) +
    geom_boxplot(alpha=0.0,outlier.shape = NA) +
    theme(text = element_text(size = 9)) +
    ggtitle(label = str_wrap(label, width = 2),subtitle=str_wrap(sub,width=20)) +
    coord_cartesian(ylim=c(plotlim_lower,plotlim_upper),clip="off")+
    geom_signif(comparisons = comparisons, map_signif_level = sig, test = 'test', test.args = list(z = comparisons, j = ifelse(label!='TNFRSF11B (2nd Instance)',label,sublabel)), 
                y_position = plotlim_bar, 
                step_increase = 0.10,  size = 0.5, 
                textsize = 1.5,
                tip_length = c(0,0)) +
    labs(color = "BMF Category", y = ifelse((ind == 1 | ind == 6 | ind == 11 | ind == 16 | ind == 21 | ind == 26),"Protein Level",""))+
    guides(colour = guide_legend(override.aes = list(size=7), title.position = 'left', nrow = 1, ncol = 4)) +
    theme(plot.margin = unit(c(0,0,plotlim_margin,0), "pt"),  
          plot.title = element_text(size=5.75), 
          plot.subtitle = element_text(size=4.5), 
          legend.title = element_text(size=10),
          legend.text = element_text(size=7),
          axis.text.x = element_blank(), 
          axis.text.y = element_text(size=7), 
          axis.title.y = element_text(size=7),
          axis.title.x = element_blank(),
          aspect.ratio = 0.95)+
    scale_fill_manual(limits = c("Constipation","Low Normal","High Normal","Diarrhea"), labels = c("Constipation","Low Normal","High Normal","Diarrhea"), values = colors(),
                    drop = FALSE)
  print(plt)
  })
}

[1] "HAVCR1"
[1] "hepatitis A virus cellular receptor 1"
[1] "HAVCR1"
[1] "HAVCR1"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TNFRSF11B"
[1] "tumor necrosis factor receptor superfamily, member 11b"
[1] "TNFRSF11B"
[1] "TNFRSF11B"

Warning in if (x < 0.001) { :
  the condition has length > 1 and only the first element will be used
Warning in if (x < 0.01) { :
  the condition has length > 1 and only the first element will be used
Warning: Removed 3 rows containing missing values (geom_signif).

[1] "HGF"
[1] "hepatocyte growth factor (hepapoietin A; scatter factor)"
[1] "HGF"
[1] "HGF"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TEK"
[1] "TEK tyrosine kinase, endothelial"
[1] "TEK"
[1] "TEK"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TNFRSF11B (2nd Instance)"
[1] "tumor necrosis factor receptor superfamily, member 11b"
[1] "TNFRSF11B"
[1] "TNFRSF11B"

Warning in if (x < 0.001) { :
  the condition has length > 1 and only the first element will be used
Warning in if (x < 0.01) { :
  the condition has length > 1 and only the first element will be used
Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TGFA"
[1] "transforming growth factor, alpha"
[1] "TGFA"
[1] "TGFA"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CSF1"
[1] "colony stimulating factor 1 (macrophage)"
[1] "CSF1"
[1] "CSF1"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "VEGFA"
[1] "vascular endothelial growth factor A"
[1] "VEGFA"
[1] "VEGFA"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "LIFR"
[1] "leukemia inhibitory factor receptor alpha"
[1] "LIFR"
[1] "LIFR"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TNFRSF9"
[1] "tumor necrosis factor receptor superfamily, member 9"
[1] "TNFRSF9"
[1] "TNFRSF9"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "IL10RB"
[1] "interleukin 10 receptor, beta"
[1] "IL10RB"
[1] "IL10RB"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "FST"
[1] "follistatin"
[1] "FST"
[1] "FST"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "GDF15"
[1] "growth differentiation factor 15"
[1] "GDF15"
[1] "GDF15"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "RARRES2"
[1] "retinoic acid receptor responder (tazarotene induced) 2"
[1] "RARRES2"
[1] "RARRES2"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "IL1RN"
[1] "interleukin 1 receptor antagonist"
[1] "IL1RN"
[1] "IL1RN"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "LDLR"
[1] "low density lipoprotein receptor"
[1] "LDLR"
[1] "LDLR"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CTSD"
[1] "cathepsin D"
[1] "CTSD"
[1] "CTSD"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "ICAM2"
[1] "intercellular adhesion molecule 2"
[1] "ICAM2"
[1] "ICAM2"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "ADA"
[1] "adenosine deaminase"
[1] "ADA"
[1] "ADA"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "PLAUR"
[1] "plasminogen activator, urokinase receptor"
[1] "PLAUR"
[1] "PLAUR"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TREML2"
[1] "triggering receptor expressed on myeloid cells-like 2"
[1] "TREML2"
[1] "TREML2"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CTSZ"
[1] "cathepsin Z"
[1] "CTSZ"
[1] "CTSZ"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CCL4"
[1] "chemokine (C-C motif) ligand 4"
[1] "CCL4"
[1] "CCL4"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "DNER"
[1] "delta/notch-like EGF repeat containing"
[1] "DNER"
[1] "DNER"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CCL13"
[1] "chemokine (C-C motif) ligand 13"
[1] "CCL13"
[1] "CCL13"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CDCP1"
[1] "CUB domain containing protein 1"
[1] "CDCP1"
[1] "CDCP1"

Warning: Removed 3 rows containing missing values (geom_signif).

#Arrange plots:
counter<<- 1
figure1 <- ggarrange(plotlist = myplots[1:10], labels = c(LETTERS[1:10]), legend = "top", align = "hv", font.label = list(size = 9.5), common.legend = TRUE, nrow = 2, ncol = 5, legend.grob = get_legend(myplots[[2]]))

[1] "TNFRSF11B"
[1] "TNFRSF11B"

Warning in if (x < 0.001) { :
  the condition has length > 1 and only the first element will be used
Warning in if (x < 0.01) { :
  the condition has length > 1 and only the first element will be used
Warning: Removed 3 rows containing missing values (geom_signif).

[1] "HAVCR1"
[1] "HAVCR1"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TNFRSF11B"
[1] "TNFRSF11B"

Warning in if (x < 0.001) { :
  the condition has length > 1 and only the first element will be used
Warning in if (x < 0.01) { :
  the condition has length > 1 and only the first element will be used
Warning: Removed 3 rows containing missing values (geom_signif).

[1] "HGF"
[1] "HGF"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TEK"
[1] "TEK"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TNFRSF11B"
[1] "TNFRSF11B"

Warning in if (x < 0.001) { :
  the condition has length > 1 and only the first element will be used
Warning in if (x < 0.01) { :
  the condition has length > 1 and only the first element will be used
Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TGFA"
[1] "TGFA"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CSF1"
[1] "CSF1"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "VEGFA"
[1] "VEGFA"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "LIFR"
[1] "LIFR"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TNFRSF9"
[1] "TNFRSF9"

Warning: Removed 3 rows containing missing values (geom_signif).

                                                                                                             
figure1

counter <<- 1
figure2 <- ggarrange(plotlist = myplots[11:20], labels = c(LETTERS[11:20]), font.label = list(size = 9.5), legend = "none", align = "hv", nrow = 2, ncol = 5)

[1] "IL10RB"
[1] "IL10RB"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "FST"
[1] "FST"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "GDF15"
[1] "GDF15"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "RARRES2"
[1] "RARRES2"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "IL1RN"
[1] "IL1RN"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "LDLR"
[1] "LDLR"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CTSD"
[1] "CTSD"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "ICAM2"
[1] "ICAM2"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "ADA"
[1] "ADA"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "PLAUR"
[1] "PLAUR"

Warning: Removed 3 rows containing missing values (geom_signif).

figure2

counter <<- 1
figure3 <- ggarrange(plotlist = myplots[21:26], labels = c(LETTERS[21:26]), font.label = list(size = 9.5), legend = "none", align = "hv", nrow = 2, ncol = 5)

[1] "TREML2"
[1] "TREML2"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CTSZ"
[1] "CTSZ"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CCL4"
[1] "CCL4"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "DNER"
[1] "DNER"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CCL13"
[1] "CCL13"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CDCP1"
[1] "CDCP1"

Warning: Removed 3 rows containing missing values (geom_signif).

figure3

counter <<- 1
ggsave(
  "BMFvsProteins1.png",
  plot = figure1,
  device = NULL,
  path = NULL,
  scale = 1.5,
  width = NA,
  height = NA,
  units = c("in", "cm", "mm", "px"),
  dpi = 300,
  limitsize = TRUE,
  bg = NULL
)

Saving 7 x 7 in image

counter <<- 1
ggsave(
  "BMFvsProteins2.png",
  plot = figure2,
  device = NULL,
  path = NULL,
  scale = 1.5,
  width = NA,
  height = NA,
  units = c("in", "cm", "mm", "px"),
  dpi = 300,
  limitsize = TRUE,
  bg = NULL
)

Saving 7 x 7 in image

counter <<- 1
ggsave(
  "BMFvsProteins3.png",
  plot = figure3,
  device = NULL,
  path = NULL,
  scale = 1.5,
  width = NA,
  height = NA,
  units = c("in", "cm", "mm", "px"),
  dpi = 300,
  limitsize = TRUE,
  bg = NULL
)

Saving 7 x 7 in image

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